5.2 AI & Machine Learning Algorithms

  • Risk assessment models: Analyze project credibility based on historical trends, team background, and market potential.

  • Automated due diligence system: Reduces human error in project evaluation.

  • Fraud detection mechanisms: Identify suspicious activities and red flags to protect investors.

    • Model: Random Forest classifier trained on 10K+ scam/non-scam projects.

    • Accuracy: 94% in detecting rug pulls (tested on 2023 data).

  • Investor Matching:

    • NLP: BERT models parse investor social profiles for risk appetite.

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